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Background information

ection 1: Introduction
a. Give a brief introduction about the assignment and search related article and write a paragraph of summary which supports your assignment. You need to give the full citation of the article.
b. Dataset 1: Give a short description about this dataset. Is this primary or secondary data What are types of variablesinvolve Explain briefly what are the possible cases used in this study.
c. Dataset 2: Explain how you collect the data and discuss its limitation (e.g. whether your sample is biased). Is this primary or secondary data? What is/are the type(s) of variable(s) involved? Give a description of cases you consider for this data set.


2. Section 2: Analysis of single variable in Dataset 1
a. To answer research question “Which type of public transport was most used by the NSW people during 8th to 14th of August 2016?”, provide a suitable numerical summary and graphical display for the variables mode of Dataset 1. Give a detailed comment to answer the research question.
b. Now to answer research question “Are there more than 50% of public transport users in NSW use the particular mode of transport found in Part a?” setup an appropriate hypotheses, perform hypotheses test and answer the research question by writing the conclusion of the test.


3. Section 3: Analysis of two variables in Dataset 1
 NSW Government need to decide on whether they have to build an underground  Railway line from either Parramatta, Bankstown or Gosford to central. To prepare a  recommendation for this;
a. Give a numerical summary and an appropriate graphical display for the variables location, by only considering those three stations; and the variable count by considering the data with trains only.
b. Perform a suitable hypothesis test at a 5% level of significance to test whether there is difference between mean counts of taps on and off.
c. Use the conclusion of the test in part b and the outputs in part a to write a recommendation to NSW government.


4. Section 4: Collect and analysis Dataset2
 You are interested in finding whether there is a difference in preference between  different gender in terms of their transport mode (Bus, Train, Ferry and Light Rail). by considering appropriate number of cases and variable, give a proper graphical  display and use it to write a comments.
Section 5: Discussion & Conclusion
 Write an executive summary by combining all your findings in the previous sections  which must be a valuable recommendation for NSW Transport. Give a suggestion  for further research

Background information

The paper is a study of the transport system in New South Wales, Australia. Data was obtained from the NSW open data for transport from the government site and a sample of the same was used to study the scope of the government to grow and improve upon the scenario as suggested from the data. The opal on and off dataset was used for the purpose of enquiry. The opal card is an all purpose transport card which can be used for travelling by ferry, light rail, bus and train by anyone who possess it.  It also provides a way to track and keep records of travel patterns of the passengers for the purpose of further developments as per the perceived issues and needs (Culnane, Rubinstein and Teague 2017).

            Ortega-Tong (2013) conducted a study using smart card data like Opal card in London, which is the Oyster card. The study used the data to classify passengers on the basis of frequency of travel and type of traveller, that is whether workers, students or even visitors who visited for business or leisure. The analysis however that was used was that of cluster analysis, done on the basis of characteristics relating to spatial variability, socio-demographic condition, activity patterns and the choice of modes.  The clusters were found to represent and classify passenger behaviour. Four clusters were found which were of visitors visiting for leisure, visitors visiting for business, registered users who use the mode regularly and those who use in more occasionally than on a regular basis.

            Hence data from smart card transactions have been proved to be useful for understanding passenger behaviour and pattern. This study focuses on the mode of transport and the frequency of tapping in and out for the state of NSW in Australia.           

            Dataset 1 is the sample of data obtained from the Opal Tap on and Tap Off Location- 8th  to 14th  August 2016 dataset, as available via the Transport or NSW Open Data. The dataset can be accessed via the link, It is therefore a secondary dataset (Creswell and Creswell 2017). The variables in the sample of size 1000 are mode of the data, with four categories, bus, train, ferry and light rail. The data also includes dates of transactions, in day, month and year. The variable tap recorded that on or off status. The location of the tap being accessed was also included. These are all categorical data, except the date variable which is interval. The variable count is interval type, giving the total number of times the tap was on or off in a certain location on that certain date.

Dataset 1: Analysis of single variable

            The second dataset was obtained by using a survey method. The data was collected using simple random sampling from travellers across NSW and hence is primary in nature. The simple random sampling method is an unbiased sample technique which gives equal chance of inclusion into the sample to all the members of a population. It is a popular probability sampling technique, considered for being simple and robust. It however can end up not being able to capture the features of the population fully if the representation of different factions in the population is not equally proportionate (Creswell and Creswell 2017).

For example if the number of students in the considered population is lower than the number of workers, then the sample could fail to gather enough information about the students. Nonetheless, it is proven to work fairly well if proper care is taken with regard to such complexities. The variables based on which data was collected are, gender, mode of transportation and the anticipated cost of public transport per month for the individual.

Section 2: Analysis of single variable in Dataset 1

            The first research question of interest is regarding the type or mode of transport for the passengers in the period 8th August , 2016 to 14th August , 2016. The following table, labelled table 1, gives the numerical summary of the passengers in each mode of transport within the given time frame.

Count of mode

Column Labels

       

Row Labels

bus

ferry

Light rail

train

Grand Total

2016-08-08

7.60%

0.00%

0.00%

6.70%

14.30%

2016-08-09

6.60%

0.40%

0.70%

7.20%

14.90%

2016-08-10

8.40%

0.30%

0.20%

7.80%

16.70%

2016-08-11

7.70%

0.40%

0.30%

7.70%

16.10%

2016-08-12

7.80%

0.60%

0.50%

8.30%

17.20%

2016-08-13

5.30%

0.50%

0.20%

5.40%

11.40%

2016-08-14

3.80%

0.40%

0.10%

5.10%

9.40%

Grand Total

47.20%

2.60%

2.00%

48.20%

100.00%

Table 1: Frequency of travel by mode

            The figure labelled 1, as given as follows, gives the graphical summary of what table 1 shows in numerical summary format.

            The  data from the numerical and graphical summary shows that the modes, train and bus have the most number of passengers in the period between 8th August and 14th August. The train had the most frequency with 48.20% opting to travel by train, closely followed by the bus with 47.20% passengers choosing to travel by bus. The ferry and the light rail were seen to have the least frequency, far less than the bus and the train with 2.60% and 2.00% respectively.

            The most popular mode of transport was therefore identified to be the train. Then it is of interest to verify whether the proportion of passengers travelling by train in NSW in the period between 8th August to 14th August was greater than 50% or 0.5 or not. This was tested for by using the binomial test for proportions (Siegel 2016). The problem could then by expressed by means of the hypothesis:

Dataset 1: Analysis of two variables

H0 : p = 0.5 against H1 : p>0.5

Here p is the proportion of people out of the total number of passengers in the given time frame who were travelling by train. The proportion was found to be equal to 0.482 as seen from table 1 or figure 1. The calculations for the same are given in the following table.

TEST FOR BINOMIAL PROPORTION

sample proportion (=p)

0.482

sample standard deviation or sd (=squared root of {np(1-p)} )

15.8011392

Z value (= squared root {1000}x(p-np)/sample sd)

-0.036023351

alpha

0.05

p value

0.48563187

CONCLUSION

Do not reject Null

Table 2: Binomial test for proportions for the percentage of passengers by train

            As per the results of the binomial test, it was concluded that there is not enough evidence to support the rejection of the null hypothesis and hence the conjecture that the percentage of people using the train in the time frame 8th to 14th August is greater than 50% was rejected, having assumed the level of significance at 5%.

Section 3: Analysis of two variables in Dataset 1

This section approaches the issue with the intention of identifying scope for expansion of the existing railway lines along Paramatta station, Gosford station and Bankstown station. The analysis of the data regarding the same is discussed as follows:

a)

The data was filtered to consider only those entries that were related to Parramatta, Gosford and Bankstown stations. The sample contained no record for Gosford however. The following table gives the numerical summary of the transportation in the stations Parramatta, Bankstown and Gosford station.

Station

Total  Count

Banks town Station

322

Parramatta Station

712

Gosford Station

0

Table 3: Activity in Parramatta, Gosford and Bankstown as found in the sample

   The following figure 2 give sthe graphical summary of the activity in the three stations of Parramatta, Gosford and Bankstown as reflected in the above table labelled 3.

   The next part of the analysis addressed the conjecture whether the number of ons and the number of offs at the two stations were same or not.  The failure of the conjecture would imply that the number of people who enter the station are same as the number who exit the station, that is the station has a steady traffic of people. The conjecture can then be expressed using the hypothesis:

H0:  mean of count of “off” = mean of count of “on”  (Null hypothesis)

Against

H1: mean of count of “off” mean of count of “on” (Alternate Hypothesis)

   The test can then be tested by assuming unequal variance for the “on” transactions and “off” transactions using independent samples t-test (Burns, Bush and Sinha 2014). The level of significance was assumed to be equal to 5 percent. Then the results of the t-test are given in the following table labelled as table 4. The two tailed test failed to reject the null hypothesis of no difference at 5 percent level of significance, indicating that the stations Parramatta and Bankstown had a steady flow of passengers both from the stations and to the stations. The station Gosfred however had no entries whatsoever.

t-Test: Two-Sample Assuming Unequal Variances

   
     

 

Count of “on”

Count of “off”

Mean

105.7431373

94.29387755

Variance

26332.70599

21546.58013

Observations

510

490

Hypothesized Mean Difference

0

 

df

994

 

t Stat

1.170944375

 

P(T<=t) one-tail

0.120950882

 

t Critical one-tail

1.646388033

 

P(T<=t) two-tail

0.241901765

 

t Critical two-tail

1.96235339

Conclusion

Table 4: Independent samples t-test for count of on/off at Parramatta and Bankstown

The two findings from the previous two parts of this section, (a) and (b) imply that the stations Parramatta and Bankstown have a steady flow of passengers who travelled to and from the respective stations. The station Parramatta was identified to have the most passenger traffic. It is therefore recommended that an underground railway line be introduced for either of these two stations, especially Parramatta.

Section 4: Collect and Analyse Dataset 2

The key issue tackled in this part was that of verifying whether there exists a bias on the basis of gender to the mode of transport a passenger may choose. A minimum sample of size 369 is required for a test with 95 percent confidence and 5% margin of error. For the current scenario, having assumed such a level of precision, a sample of size 370 was collected (Creswell and Creswell 2017). The variables gender, preferred mode of travel and an additional variable of anticipated monthly expense on transport was collected by means of a survey from residents of NSW. The findings of the survey are hence discussed.

It was seen that 49.46 percent of the participants were males as denoted by M and 50.54 percent were females denoted by F. The distribution of the participants by gender was therefore close to being equal.

            The most preferred transportation mode was identified to be the bus with 35.14 percent choosing bus as per the survey followed by the train with 31.35 percent reporting train as their transport of choice. 15.95 percent said that they preferred the light rail while 17.57 percent chose the Ferry.

            Among the total female passengers, 33.16 percent were chose the bus, 17.65 percent chose the ferry, 20.32 percent chose the light rail and 28.88 percent chose the train. 37.16 percent. 37.16 percent of males were found to choose the bus, 17.49 percent chose the ferry, 11.48 percent chose the light rail and 33.88 percent chose the train.

            The expected monthly cost of fare for those travelling by train was found to be highest with $172.76, followed by the bus with $151.38 per month and then the light rail with $91.02 and ferry with $80.77. This is perhaps because the bus and the train offer the longest distance of travel as compared to the other two.  The overall monthly expenditure was found to be $136.05.

This was computed by taking the value of the midpoints of the intervals of expense per month for each mode and by finding the sum of product of these points with the frequency for each class interval which were recorded, divided by total count of each mode (Rumsey 2015). The same method was repeated by using pivot table to add gender to the column field and then compute the expectations for each gender (Berenson et al. 2012).

            The findings suggest that the bus is favoured first and the train second by both the men and the women. However it seems that women prefer the light rail to the ferry whereas the opposite is seen for the males.

Section 5: Discussion

            The study in its analysis of the transport conditions at NSW employed two datasets one secondary and one primary to explore the possibilities of further development. As per the secondary data, based on the opal card data available via the transport NSW open data, it is seen that trains are the most favoured mode of transport followed by the bus.  However it was found that the proportion of people who prefer the train is not greater than 50 percent. The primary data however suggests that it is actually the bus which is most preferred. Nonetheless, both the data indicated that the bus and the train are the two most favoured modes with pretty close preference proportions.

The study identified Parramatta and Bankstown as potential candidates where underground railways could be built. Parramatta was found to be more suitable however. Using the primary data analysis, among the females, it was found that bus is the most preferred followed by the train. This was reflected by the males as well. However the females seemed to prefer the light rail more than the ferry and the males preferred the ferry over the rail.

References

Berenson, M., Levine, D., Szabat, K.A. and Krehbiel, T.C., 2012. Basic business statistics: Concepts and applications. Pearson higher education AU.

Burns, A.C., Bush, R.F. and Sinha, N., 2014. Marketing research (Vol. 7). Harlow: Pearson.

Creswell, J.W. and Creswell, J.D., 2017. Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications.

Culnane, C., Rubinstein, B.I. and Teague, V., 2017. Privacy assessment of de-identified opal data: A report for transport for NSW. arXiv preprint arXiv:1704.08547.

Ortega-Tong, M.A., 2013. Classification of London's public transport users using smart card data (Doctoral dissertation, Massachusetts Institute of Technology).

Rumsey, D.J., 2015. U Can: statistics for dummies. John Wiley & Sons.

Siegel, A., 2016. Practical business statistics. Academic Press.   

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My Assignment Help. (2021). Transport System In New South Wales, Australia: A Study Of Passenger Behaviour And Patterns. Retrieved from https://myassignmenthelp.com/free-samples/bus708-statistics-and-data-analysis/purpose-transport.html.

"Transport System In New South Wales, Australia: A Study Of Passenger Behaviour And Patterns." My Assignment Help, 2021, https://myassignmenthelp.com/free-samples/bus708-statistics-and-data-analysis/purpose-transport.html.

My Assignment Help (2021) Transport System In New South Wales, Australia: A Study Of Passenger Behaviour And Patterns [Online]. Available from: https://myassignmenthelp.com/free-samples/bus708-statistics-and-data-analysis/purpose-transport.html
[Accessed 29 May 2024].

My Assignment Help. 'Transport System In New South Wales, Australia: A Study Of Passenger Behaviour And Patterns' (My Assignment Help, 2021) <https://myassignmenthelp.com/free-samples/bus708-statistics-and-data-analysis/purpose-transport.html> accessed 29 May 2024.

My Assignment Help. Transport System In New South Wales, Australia: A Study Of Passenger Behaviour And Patterns [Internet]. My Assignment Help. 2021 [cited 29 May 2024]. Available from: https://myassignmenthelp.com/free-samples/bus708-statistics-and-data-analysis/purpose-transport.html.

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